CBS 2019
CBSMD教育中心
English

充血性心力衰竭

科研文章

荐读文献

Rationale and design of the comParIson Of sacubitril/valsartaN versus Enalapril on Effect on nt-pRo-bnp in patients stabilized from an acute Heart Failure episode (PIONEER-HF) trial Novel percutaneous interventional therapies in heart failure with preserved ejection fraction: an integrative review In acute HF and iron deficiency, IV ferric carboxymaltose reduced HF hospitalizations, but not CV death, at 1 y Nuclear Imaging of the Cardiac Sympathetic Nervous System: A Disease-Specific Interpretation in Heart Failure The Future of Biomarker-Guided Therapy for Heart Failure After the Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT) Study Effects of Dapagliflozin on Symptoms, Function and Quality of Life in Patients with Heart Failure and Reduced Ejection Fraction: Results from the DAPA-HF Trial Clinical epidemiology of heart failure with preserved ejection fraction (HFpEF) in comparatively young hospitalized patients Impact of Myocardial Scar on Prognostic Implication of Secondary Mitral Regurgitation in Heart Failure Heart Failure With Preserved Ejection Fraction in the Young Stage B heart failure: management of asymptomatic left ventricular systolic dysfunction

Review Article2020 Jul 16;229:1-17.

JOURNAL:Am Heart J . Article Link

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

CR Olsen, RJ Mentz, KJ Anstrom et al. Keywords: machine learning; artificial intelligence;

ABSTRACT

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.